"""Конфигурация — pydantic models + YAML loader. Структура YAML: broker: host: localhost port: 1883 username_env: MQTT_USERNAME password_env: MQTT_PASSWORD instances: - name: livingroom_tv zmq_endpoint: tcp://127.0.0.1:5555 default_layout: quad ha_discovery: enabled: true prefix: homeassistant device_name: "CUDA Grid Composer" http: host: 0.0.0.0 port: 8080 log: level: INFO """ from __future__ import annotations import os from pathlib import Path from typing import Self import yaml from pydantic import BaseModel, Field, field_validator class BrokerCfg(BaseModel): host: str = "localhost" port: int = 1883 client_id: str = "cuda-grid-controller" username_env: str | None = None password_env: str | None = None keepalive_sec: int = 30 @property def username(self) -> str | None: return os.environ.get(self.username_env) if self.username_env else None @property def password(self) -> str | None: return os.environ.get(self.password_env) if self.password_env else None class InstanceCfg(BaseModel): """Один FFmpeg pipeline = одна cuda_grid filter instance.""" name: str = Field(description="уникальное имя — становится частью HA entity ID") zmq_endpoint: str = Field( description="ZMQ endpoint FFmpeg's zmq filter (tcp://host:port)" ) default_layout: str = "quad" filter_target: str = Field( default="Parsed_cuda_grid_0", description="Filter target name в FFmpeg filter graph (для process_command)", ) @field_validator("name") @classmethod def name_alnum(cls, v: str) -> str: if not v.replace("_", "").isalnum(): raise ValueError(f"instance name '{v}' must be alphanumeric + underscore") return v class HaDiscoveryCfg(BaseModel): enabled: bool = True prefix: str = "homeassistant" device_name: str = "CUDA Grid Composer" device_identifier: str = "cuda_grid_controller" class HttpCfg(BaseModel): host: str = "0.0.0.0" port: int = 8080 class LogCfg(BaseModel): level: str = "INFO" class Config(BaseModel): broker: BrokerCfg = BrokerCfg() instances: list[InstanceCfg] = [] ha_discovery: HaDiscoveryCfg = HaDiscoveryCfg() http: HttpCfg = HttpCfg() log: LogCfg = LogCfg() @classmethod def from_yaml(cls, path: Path | str) -> Self: with open(path) as f: data = yaml.safe_load(f) or {} return cls.model_validate(data)